While the concept of artificial intelligence has been explored for some time, the modern applications of artificial intelligence have exploded such that artificial intelligence is being integrated into many devices and decision-making models to improve their learning, reasoning, data processing capabilities, and the like of the devices. The most apparent and broad applications of artificial intelligence include machine learning, natural language processing, computer vision, robotics, knowledge reasoning, planning, and general artificial intelligence.
To be effective, many of the above-noted broad applications of artificial intelligence require the consumption of extremely large data sets in the initial training of the artificial intelligence algorithms (e.g., deep learning, recurrent neural networks, etc.) being implemented in the specific applications and/or devices (e.g., autonomous vehicles, medical diagnostics, etc.). Because the data sets used in training are often very large and the underlying computer architecture may not be specifically designed for artificial intelligence training, the training of an artificial intelligence algorithm may require thousands of hours of data processing by the underlying computer architecture. While it may be possible to scale or increase the number of computers or servers used in ingesting data sets for training an artificial intelligence algorithm, this course of action often proves to not be economically feasible.
Similar data processing issues arise in the implementation or execution of the artificial intelligence algorithms due to the large amount of data being captured such as data originating from billions of Internet transactions, remote sensors for computer vision, and the like. The modern remote distributed networked servers (e.g., the cloud) and onboard computer processors (e.g., GPUs, CPUs, etc.) appear to be inadequate for ingesting and processing such great volumes of data efficiently to maintain pace with the various implementations of the artificial intelligence algorithms.
Accordingly, there is a need in the semiconductor space and specifically in the computer chip architecture field for an advanced computing processor, computing server, or the like that is capable of rapidly and efficiently ingesting large volumes of data for at least the purposes of allowing enhanced artificial intelligence algorithms and machine learning models and training to be implemented. Additionally, these advanced computing systems may function to enable improved data processing techniques and related or similar complex and processor-intensive computing to be achieved.
The inventors of the inventions described in the present application have designed an integrated circuit architecture that allows for enhanced data processing capabilities and have further discovered related methods and architectures for fabricating the integrated circuit(s), packaging the integrated circuit(s), powering/cooling the integrated circuit(s), and the like.
The below-described embodiments of the present application provide such advanced and improved computer chip architecture and related IC fabrication techniques.